"""simple docstring"""


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase=None      ,					_lowerCamelCase="resnet50"      ,					_lowerCamelCase=3      ,					_lowerCamelCase=32      ,					_lowerCamelCase=3      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					):
										lowerCamelCase__   =parent
										lowerCamelCase__   =out_indices if out_indices is not None else [4]
										lowerCamelCase__   =stage_names
										lowerCamelCase__   =out_features
										lowerCamelCase__   =backbone
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =image_size
										lowerCamelCase__   =num_channels
										lowerCamelCase__   =use_pretrained_backbone
										lowerCamelCase__   =is_training

				def 						_a      ( self ):
										lowerCamelCase__   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
										lowerCamelCase__   =self.get_config()

										return config, pixel_values

				def 						_a      ( self ):
										return TimmBackboneConfig(
										    image_size=self.image_size      ,					num_channels=self.num_channels      ,					out_features=self.out_features      ,					out_indices=self.out_indices      ,					stage_names=self.stage_names      ,					use_pretrained_backbone=self.use_pretrained_backbone      ,					backbone=self.backbone      ,					)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =TimmBackbone(config=_lowerCamelCase )
										model.to(_lowerCamelCase )
										model.eval()
										with torch.no_grad():
																lowerCamelCase__   =model(_lowerCamelCase )
										self.parent.assertEqual(
										    result.feature_map[-1].shape      ,					(self.batch_size, model.channels[-1], 14, 14)      ,					)






				def 						_a      ( self ):
										lowerCamelCase__   =self.prepare_config_and_inputs()
										lowerCamelCase__   , lowerCamelCase__   =config_and_inputs
										lowerCamelCase__   ={"pixel_values": pixel_values}
										return config, inputs_dict






@require_torch
@require_timm
class   __UpperCAmelCase		(      __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase      ):
				A__ :  Tuple    				=			(TimmBackbone,) if is_torch_available() else ()
				A__ :  Optional[int]    				=			{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
				A__ :  Any    				=			False
				A__ :  Dict    				=			False
				A__ :  Optional[Any]    				=			False
				A__ :  Optional[Any]    				=			False

				def 						_a      ( self ):
										lowerCamelCase__   =TimmBackboneModelTester(self )
										lowerCamelCase__   =ConfigTester(self      ,					config_class=_lowerCamelCase      ,					has_text_modality=_lowerCamelCase )

				def 						_a      ( self ):
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()

				def 						_a      ( self ):
										lowerCamelCase__   ="resnet18"
										lowerCamelCase__   ="microsoft/resnet-18"

										lowerCamelCase__   =AutoBackbone.from_pretrained(_lowerCamelCase      ,					use_timm_backbone=_lowerCamelCase )
										lowerCamelCase__   =AutoBackbone.from_pretrained(_lowerCamelCase )

										self.assertEqual(len(timm_model.out_features )      ,					len(transformers_model.out_features ) )
										self.assertEqual(len(timm_model.stage_names )      ,					len(transformers_model.stage_names ) )
										self.assertEqual(timm_model.channels      ,					transformers_model.channels )
										# Out indices are set to the last layer by default. For timm models, we don't know
										# the number of layers in advance, so we set it to (-1,), whereas for transformers
										# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
										self.assertEqual(timm_model.out_indices      ,					(-1,) )
										self.assertEqual(transformers_model.out_indices      ,					[len(timm_model.stage_names ) - 1] )

										lowerCamelCase__   =AutoBackbone.from_pretrained(_lowerCamelCase      ,					use_timm_backbone=_lowerCamelCase      ,					out_indices=[1, 2, 3] )
										lowerCamelCase__   =AutoBackbone.from_pretrained(_lowerCamelCase      ,					out_indices=[1, 2, 3] )

										self.assertEqual(timm_model.out_indices      ,					transformers_model.out_indices )
										self.assertEqual(len(timm_model.out_features )      ,					len(transformers_model.out_features ) )
										self.assertEqual(timm_model.channels      ,					transformers_model.channels )

				@unittest.skip("TimmBackbone doesn't support feed forward chunking" )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone initialization is managed on the timm side" )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
				def 						_a      ( self ):
										pass

				@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
				def 						_a      ( self ):
										pass

				@unittest.skip("model weights aren't tied in TimmBackbone." )
				def 						_a      ( self ):
										pass

				@unittest.skip("model weights aren't tied in TimmBackbone." )
				def 						_a      ( self ):
										pass

				@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
				def 						_a      ( self ):
										pass

				@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
				def 						_a      ( self ):
										pass

				@unittest.skip("TimmBackbone doesn't support output_attentions." )
				def 						_a      ( self ):
										pass

				@unittest.skip("Safetensors is not supported by timm." )
				def 						_a      ( self ):
										pass

				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
				def 						_a      ( self ):
										pass

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__   =model_class(_lowerCamelCase )
																lowerCamelCase__   =inspect.signature(model.forward )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowerCamelCase__   =[*signature.parameters.keys()]

																lowerCamelCase__   =["pixel_values"]
																self.assertListEqual(arg_names[:1]      ,					_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__   =True
										lowerCamelCase__   =self.has_attentions

										# no need to test all models as different heads yield the same functionality
										lowerCamelCase__   =self.all_model_classes[0]
										lowerCamelCase__   =model_class(_lowerCamelCase )
										model.to(_lowerCamelCase )

										lowerCamelCase__   =self._prepare_for_class(_lowerCamelCase      ,					_lowerCamelCase )
										lowerCamelCase__   =model(**_lowerCamelCase )
										lowerCamelCase__   =outputs[0][-1]

										# Encoder-/Decoder-only models
										lowerCamelCase__   =outputs.hidden_states[0]
										hidden_states.retain_grad()

										if self.has_attentions:
																lowerCamelCase__   =outputs.attentions[0]
																attentions.retain_grad()

										output.flatten()[0].backward(retain_graph=_lowerCamelCase )

										self.assertIsNotNone(hidden_states.grad )

										if self.has_attentions:
																self.assertIsNotNone(attentions.grad )






				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowerCamelCase__   =model_class(_lowerCamelCase )
																model.to(_lowerCamelCase )
																model.eval()
																lowerCamelCase__   =model(**_lowerCamelCase )

																self.assertEqual(len(result.feature_maps )      ,					len(config.out_indices ) )
																self.assertEqual(len(model.channels )      ,					len(config.out_indices ) )

																# Check output of last stage is taken if out_features=None, out_indices=None
																lowerCamelCase__   =copy.deepcopy(_lowerCamelCase )
																lowerCamelCase__   =None
																lowerCamelCase__   =model_class(_lowerCamelCase )
																model.to(_lowerCamelCase )
																model.eval()
																lowerCamelCase__   =model(**_lowerCamelCase )

																self.assertEqual(len(result.feature_maps )      ,					1 )
																self.assertEqual(len(model.channels )      ,					1 )

																# Check backbone can be initialized with fresh weights
																lowerCamelCase__   =copy.deepcopy(_lowerCamelCase )
																lowerCamelCase__   =False
																lowerCamelCase__   =model_class(_lowerCamelCase )
																model.to(_lowerCamelCase )
																model.eval()
																lowerCamelCase__   =model(**_lowerCamelCase )



"""simple docstring"""


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       **__lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =AutoConfig.from_pretrained(__lowerCAmelCase  ,       **__lowerCAmelCase							)
						lowerCamelCase__   =AutoModelForSeqaSeqLM.from_config(__lowerCAmelCase							)
						model.save_pretrained(__lowerCAmelCase							)
						AutoTokenizer.from_pretrained(__lowerCAmelCase							).save_pretrained(__lowerCAmelCase							)
						return model


if __name__ == "__main__":
	fire.Fire(save_randomly_initialized_version)


